code: def compile_results(
      self,
      *,
      batch: NestedDict,
      fwd_out: Mapping[str, Any],
      loss_per_module: Mapping[str, TensorType],
      metrics_per_module: DefaultDict[ModuleID, Dict[str, Any]],
  ) -> Mapping[str, Any]:
      results = super().compile_results(
          batch=batch,
          fwd_out=fwd_out,
          loss_per_module=loss_per_module,
          metrics_per_module=metrics_per_module,
      )
      # This is to check if in the multi-gpu case, the weights across workers are
      # the same. It is really only needed during testing.
      if self.config.report_mean_weights:
          mean_ws = {}
          for module_id in self.module.keys():
              m = self.module[module_id]
              parameters = convert_to_numpy(self.get_parameters(m))
              mean_ws[module_id] = np.mean(
                  [w.mean() for w in parameters]
              )
              results[module_id]["mean_weight"] = mean_ws[module_id]
  
      return results
  
file_path: ray/rllib/core/testing/testing_learner.py
imports: from typing import Any, DefaultDict, Dict, Mapping, Type
  import numpy
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig
  from ray.rllib.core.learner.learner import Learner
  from ray.rllib.core.rl_module.marl_module import MultiAgentRLModule, MultiAgentRLModuleSpec
  from ray.rllib.core.rl_module.rl_module import SingleAgentRLModuleSpec
  from ray.rllib.policy.sample_batch import DEFAULT_POLICY_ID
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.nested_dict import NestedDict
  from ray.rllib.utils.numpy import convert_to_numpy
  from ray.rllib.utils.typing import ModuleID, RLModuleSpec, TensorType
label_desc: Use image node_id 1 for calling the BaseTestingLearner obj's underlying member method code with example usage: obj.compile_results() and returns: results